• Coupled systems mechanics
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• v.5 no.1
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• pp.21-39
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• 2016

Wastewater process models are the essential tools for understanding relevant aspects of wastewater treatment system. Wastewater process modeling provides more options for upgrades and better understanding of new plant design, as well as improvements of operational controls. The software packages (BioWin, GPS-X, Aqua designer, etc) solve a series of simulated equations simultaneously in order to propose several solutions for a specific facility. Research and implementation of wastewater process modeling in combination with computational fluid dynamics enable testing for improvements of flow characteristics for WWTP and at the same time exam biological, physical, and chemical characteristics of the flow. Application of WWTP models requires broad knowledge of the process and expertise in modeling. Therefore, an efficient and good modeling practice requires both experience and set of proper guidelines as a background.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.200-211
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• 1997

Most recently, offshore Engineering & Construction field is concerned about integration management technology such as CIM(Computer Integrated Manufacturing), PDM(Product Data Management) and Enterprise Information Engineering in order to cope with the rapid change of engineering and manufacturer specification as per owner's requirement during construction stage of the project. System integration and integrated data modeling with relational database in integration management technology improve the quality of product and reduce the period of the construction project by reason of owing design information jointly. This paper represents the design methodology of system integration using Business Process Reengineering by the case study. The case study is about the offshore plant material information process from front end engineering design to detail engineering for the construction and the basis of monitoring system by integrating and sharing the design information between the 2D intelligent P&ID and 3D plant modeling using relational database. As a result of the integrated data modeling and system integration, it is possible to maintain the consistency of design process in point of view of the material balancing and reduce the design assumption/duration. Near future, this system will be expanded and connected with the MRP(Material Requirement Planing) and the POR (Purchase Order Requisition) system.

• Journal of the Korean Society of Combustion
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• v.23 no.1
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• pp.44-54
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• 2018

Heat and mass balance process modeling has been conducted for a coal-firing power plant to be used as a testbed facility for development of various plant systems and equipment. As the material and design of the boiler tube bundle and fuel conversion to the biomass have become major concerns, the process modeling is required to incorporate those features in its calculation. The simulation cases for two different generation load show the satisfying results compared to the operational data from the actual system. Based on the established process conditions, the hypothetical case using wood pellet has also been simulated. Additional calculations for the tube bundle has been conducted regarding the changes in the tube material and design.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.551-560
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• 1998

This paper presents a neural network model representing complex hydro-thermo-dynamic characteristics of a steam generator in nuclear power plants. The key modeling processes include training data gathering process, analysis of system dynamics and determining of the neural network structure, training process, and the final process for validation of the trained model. In this paper, we suggest a training data gathering method from an unstable steam generator so that the data sufficiently represent the dynamic characteristics of the plant over a wide operating range. In addition, we define the inputs and outputs of neural network model by analyzing the system dimension, relative degree, and inputs/outputs of the plant. Several types of neural networks are applied to the modeling and training process. The trained networks are verified by using a class of test data, and their performances are discussed.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1844-1854
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• 2003

This paper discusses details of modeling and robust control of an AMB (active magnetic bearing) spindle, and part I presents a modeling and validation process of the AMB spindle. There are many components in AMB spindle : electromagnetic actuator, sensor, rotor, power amplifier and digital controller. If each component is carefully modeled and evaluated, the components have tight structured uncertainty bounds and achievable performance of the system increases. However, since some unknown dynamics may exist and the augmented plant could show some discrepancy with the real plant, the validation of the augmented plant is needed through measuring overall frequency responses of the actual plant. In addition, it is necessary to combine several components and identify them with a reduced order model. First, all components of the AMB spindle are carefully modeled and identified based on experimental data, which also render valuable information in quantifying structured uncertainties. Since sensors, power amplifiers and discretization dynamics can be considered as time delay components, such dynamics are combined and identified with a reduced order. Then, frequency responses of the open-loop plant are measured through closed-loop experiments to validate the augmented plant. The whole modeling process gives an accurate nominal model of a low order for the robust control design.

• Environmental Engineering Research
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• v.10 no.2
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• pp.88-103
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• 2005

In this paper, data-driven modeling and multiresolution analysis (MRA) are applied for a full-scale wastewater treatment plant (WWTP). The proposed method is based on modeling by partial least squares (PLS) and multiscale monitoring by a generic dissimilarity measure (GDM), which is suitable for nonstationary and non-normal process monitoring such as a biological process. Case study in an industrial plant showed that the PLS model could give good modeling performance and analyze the dynamics of a complex plant and MRA was useful to detect and isolate various faults due to its multiscale nature. The proposed method enables us to show the underlying phenomena as well as to filter out unwanted and disturbing phenomena.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1449-1454
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• 2003

For many industrial processes there are good static models used for process design and steady state operation. By using system identification techniques, it is possible to obtain black-box models with reasonable complexity that describe the system well in specific operating conditions [1]. But black-box models using inductive modeling(IM) is not suitable for model based control because they are only valid for specific operating conditions. Thus we need to use deductive modeling(DM) for a wide operating range. Furthermore, deductive modeling is several merits: First, the model is possible to be modularized. Second, we can increase and decrease the model complexity. Finally, we are able to use model for plant design. Power plant must be able to operate well at dramatic load change and consider safety and efficiency. This paper proposes a simplified nonlinear model of an industrial boiler, one of component parts of a power plant, by DM method and applies optimal control to the model.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.555-561
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• 2009

The established mathematical modeling methods have limitation to know the hydraulic characteristics at the wastewater treatment plant which are complex and nonlinear systems. So, an artificial neural network (ANN) model based on hydraulic characteristics is applied for modeling wastewater quality of a full-scale wastewater treatment plant using DNR (Daewoo nutrient removal) process. ANN was trained using data which are influents (TSS, BOD, COD, TN, TP) and effluents (COD, TN, TP) components in a year, and predicted the effluent results based on the training. To raise the efficiency of prediction, inputs of ANN are added the influent and effluent information that are in yesterday and the day before yesterday. The results of training data tend to have high accuracy between real value and predicted value, but test data tend to have lower accuracy. However, the more hydraulic characteristics are considered, the results become more accuracy.

• IE interfaces
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• v.8 no.3
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• pp.61-73
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• 1995

As the level of maunfacturing system automation increases, the issues of modeling and simulation of AMS(Automated Manufacturing System) are becoming more important. Proposed in this paper is the JR-Net(Job Resource relation-Net) modeling framework which naturally mimics the process of designing an AMS by FA(Factory Automation) engineers. Its main purpose is to provide a modeling tool which facilitate modeling work of AMS for FA engineers unfamiliar with simulation modeling. The proposed modeling scheme is based on the extensive observation that typical AMSs are built from the set of 'standard' components(or catalog items). As an application of the proposed model, two real examples of FMS('G7'FMS model plant, RPI FMS) are modeled by JR-Net, and in case of FMS model plant, a simulation program development procedure using JR-Net modeling results is explained. Finally, simulation result of FMS model plant is analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.179-182
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• 1997

In a training simulator for power plant, operator's action in the MCR(Main Control Room) are given to plant process and computer system model as an inputs, and the same response as in real power plant is provided in real time. Inter-process communication and synchronization are especially important among various inputs. In the plant simulator, to simulate the digital control system such as FOXBORO SPEC-200 Micro control system, modification and adaptation of control card(CCC) and its continuous display station(CDS) is necessary. This paper describes the modeling and simulation of FOXBORO SPEC-200 Micro control system applied to Younggwang nuclear power plant unit #3 & 4, and its integration process to the full-scope replica type training simulator. In a simulator, display station like CDS of FOXBORO SPEC-200 Micro control system is classified as ITI(Intelligent Type Instrument), which has a micro processor inside to process information and the corresponding alphanumeric display, and the stimulation of ITI limits the important functions in a training simulator such as backtrack, replay, freeze and IC reset. Therefore, to achieve the better performance of the simulator, modification of CDS and special firmware is developed to simulate the FOXBORO SPEC-200 Micro control system. Each control function inside control card is modeled and simulated in generic approach to accept the plant data and control parameter conveniently, and debugging algorithms are applied for massive coding developed in short period.